To segment multiple OARs with different sizes from CT images with low contrast and anisotropic resolution, we propose a novel framework consisting of Segmental Linear Function (SLF)-based intensity transform and a 3D-SepNet with hard-voxel weighting for training. For intensity transform, the traditional method of Naive Linear Function (NLF) may not be effective enough because it cannot obtain good visibility for multiple OARs including both soft tissues and bones at the same time. In contrast, our SLF makes multiple OARs have a good visibility at the same time. Considering the clinical fact that radiologists would view scans under different window widths/levels to better delineate different organs, we also applied multiple SLFs to better segment different tissues. Generally, our SLF for intensity transform is also applicable to other tasks, such as segmentation of multiple organs from thoracic or abdominal CT images [1]}.
